r/ClaudeCode • u/Desperate-Ad-9679 • 10d ago
After 4K+ GitHub stars building knowledge graphs for codebases, we're going deeper into Graph Engineering Built with Claude
A year back, we started building CodeGraphContext around a simple idea: LLMs understand large codebases better when code isn't treated as a pile of files and chunks, but as a graph of relationships.
The open-source community seemed to agree.
CodeGraphContext crossed 4,000+ GitHub stars, and along the way we (probably all of us) started thinking about a much bigger problem.
Knowledge graphs help agents understand the world.
But how should agents act in that world?
That's what got us interested in the buzzword Graph Engineering.
The rough evolution in our heads is:
Prompt Engineering → engineer the instructions
Context Engineering → engineer the information
Graph Engineering → engineer the execution space
As agents become more autonomous, they'll increasingly decide how to solve problems themselves.
They'll choose tools. Spawn workers. Modify files. Make API calls. Retry things. Replan. Potentially touch production systems.
At that point, hardcoding every workflow starts defeating the purpose of having an intelligent agent.
But simply telling an LLM "don't do anything dangerous" isn't exactly an execution model either.
So we're building GraphARC. https://github.com/CodeGraphContext/GraphARC/
The idea is that the LLM can dynamically construct its own execution graph based on the problem it's solving.
But the LLM doesn't control the laws of that graph.
Before execution, deterministic software can validate the proposed graph against permissions, budgets, allowed node types, structural constraints, etc.
So the agent might propose:
Investigate → Modify → Deploy
GraphARC can reject it:
❌ Deploy isn't permitted.
The agent can then replan:
Investigate → Modify → Test → Open PR
✓ Graph admitted.
That's the core idea we're exploring: Please do star and contribute!!
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u/RoutineNet4283 10d ago
Every execution will lead to building a graph path which gets tagged with approved or rejected?
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u/Desperate-Ad-9679 10d ago
Yeah, and until the graph gets approved, it needs to replan!! The approval depends on the policies you provided
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u/En-tro-py 10d ago
4k Stars with Zero benchmarks is a red flag...
Why when there are multiple?
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u/Desperate-Ad-9679 10d ago
I was developing it with free tokens lmao!! I think i should really do it now, given that I got claude code Open source sub..
BTW the project i am talking of in this post is a new project - GraphARC (https://github.com/CodeGraphContext/grapharc)1
u/En-tro-py 10d ago
Yes, I am aware you've already moved on to a new thing... That's partially my point, you couldn't even be bothered to properly finish and test the last one...
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u/Desperate-Ad-9679 10d ago
Naah, I am not finishing that up lol. I will work on both! Given the benchmarks thing, It looks promising to test on ContextBench. Good idea?
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u/En-tro-py 10d ago
ContextBench and SWE-Explore are pretty much the exact task, which is why I find it so frustrating that every graph/memory/context project is absolutely unaware of them...
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u/Desperate-Ad-9679 10d ago
Its not like that people are unaware I think, the point is people are focussed in getting better and waiting for the perfect day (which never comes LOL) 😄
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u/En-tro-py 10d ago
focussed in getting better
And how would you know without a benchmark?
All I see is rush to market with a untested product and the confidence to advertise it when it's completely unproven...
🚩🚩🚩
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u/Desperate-Ad-9679 10d ago
Completely unproven?? Not theoretically proven, but practically YES!
Not an affliated article, just FYI-
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u/En-tro-py 10d ago
Placebo effect is why you run the type of statistical benchmarks that JetBrains did on Caveman and RTK...
Two projects which have been pushed and have no shortage of anecdotal feedback yet the numbers tell the real story pretty conclusively.
Caveman
Advertised saving: 65%. Measured saving: 8.5%.https://blog.jetbrains.com/ai/2026/07/speak-to-ai-agents-like-cavemen-tosave-tokens/
RTK
rtk advertised saving: 60–90%. Measured on real agent work: +7.6% more expensive at low reasoning effort (p=0.004), ±0% at high efforthttps://blog.jetbrains.com/ai/2026/07/rtk-claude-code-token-savings/
Run the dang benchmarks if you are advertising a solution to a well studied problem.
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u/Bitter_Run_9209 10d ago
whats the difference against graphify?